A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo₄ battery

Abstract

Summary LiFePo4 battery is widely used in electric vehicles; however, its flatness and hysteresis of the open‐circuit voltage curve pose a big challenge to precise state of charge (SOC) estimation. The issue is discussed and addressed in this paper. First, a cell model with hysteresis is built to describe real‐time dynamic characteristics of the LiFePo4 battery. Second, the model parameters and SOC are estimated independently to avoid the possibility of cross interference between them. For model identification, an adaptive unscented Kalman filter (AUKF) algorithm is used to identify the cell parameters as they change slowly. While SOC could change rapidly, wavelet transform AUKF algorithm is put forward to estimate SOC. In the novel algorithm, the measurement noise can be estimated and updated online. Finally, the performance of the proposed method is verified under dynamic current condition. The experimental results show that estimated value based on the proposed method is more accurate than unscented Kalman filter‐based method and AUKF‐based algorithm. Meanwhile, the proposed estimator also has the merits of fast convergence and good robustness against the initialization uncertainty.

Two‐layer online state‐of‐charge estimation of lithium‐ion battery with current sensor bias correction

“…Where i R 1 and i are the currents through R 1 and R 0 , respectively. Shifting (24) to the earlier adjacent time step yields:…”

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

“…However, this method has problems such as the initial integral value is uncertain, the battery capacity is not constant, and the error accumulates with time. Kalman filtering algorithm requires observation of the input and output results of the system, which are then used to estimate the current state using the state equation . However, the accuracy of this algorithm depends on having an accurate battery model and establishment of the state equation.…”

Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

“…4,5 The accurate SOC and SOH estimation for lithium-ion batteries-based ESSs not only keeps the battery from overcharging and overdischarging but also provides the cell property alteration over its whole service life. Model-based method is another widely utilized approach, in which the Kalman filtering family, including Kalman filter (KF), 12 the extended Kalman filter (EKF), 13 and unscented Kalman filter (UKF), 14,15 has drawn much more attention to be applied to batteries SOC estimation. Simultaneously, irreversible physical and chemical alterations occur during its whole lifespan, which will lead to capacity decrease and resistance increase.…”

“…11 In the OCV methods, the OCV can only be measured at equilibrium state (ie, the cell is idle for a few minutes), which is not suitable for real-time application. Model-based method is another widely utilized approach, in which the Kalman filtering family, including Kalman filter (KF), 12 the extended Kalman filter (EKF), 13 and unscented Kalman filter (UKF), 14,15 has drawn much more attention to be applied to batteries SOC estimation. This kind of methods has achieved excellent performance for onboard application.…”